TFM2_16S_amplicons_analysis
lrubinat
5/30/2017
1) 16S amplicons
1.1) Data overview
The dataset “Taraspina 18S miTags” contains reads from 122 samples of Malaspina. On average, each sample contains 45940 OTUs:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 405 20410 33100 45940 68340 148200
Overall reads per sample:
1.2) Normalization
In order to keep as many samples as possible, we rarefy at 16509 reads per sample. By that, we loose 31 samples, and after removing the exluded samples in the 18S dataset (to make them comparable), we end up with a normalized dataset containing 91 samples and 8881 OTUs.
Datasets summary:
dim(tb16_tax) #original dataset## [1] 9114 128dim(tb16_tax_occur) #original dataset with occurrence data alone## [1] 9114 122dim(tb16_tax_occur_min16509) #dataset without samples with less than 16509 OTUs## [1] 8881 91dim(tb16_tax_occur_ss16509_no_cero) #rarefied dataset## [1] 91 82041.3) General community analysis
1.3.1) Richness and evenness (Shannon index)
Most of the samples take Shannon Index values between 2.5 and 3.5:
1.3.2) Richness: OTU number
Lowest number of OTUs per sample:
## [1] 600Maximum number of OTUs per sample:
## [1] 1655In most of the samples, we can identify about 1400 OTUs:
1.3.3) Index of evenness
1.3.3.1) Pielou’s index
The Pielou index (constrained between 0 and 1) takes values closer to 1 as the variation of species proportion in a sample increases. Our samples get values around 0.6, meaning that the numerical composition of different OTUs in a sample is not so variable - we might observe certain dominant species.
1.3.4) Abundance Models
Most of the OTUs show very few occurrences, suggesting that we will probably be able to identify a significant ammount of rare otus:
1.3.4.1) Rank-Abundance or Dominance/Diversity Model (“radfit”)
The OTUs abundance distribution fits relativelly close to log-normal model.
1.3.4.2) Preston’s Lognormal Model
According to Preston’s lognormal model fit into species frequencies groups, we’re missing ~262 species:
veiledspec(tb16_tax_occur_ss16509_prestonfit)## Extrapolated Observed Veiled
## 9143.6079 8881.0000 262.6079
When computing Prestons’ lognormal model fit without pooling data into groups, we miss ~251 species:
## Extrapolated Observed Veiled
## 9132.6917 8881.0000 251.69171.3.5) Rarefaction curves of rarefied and non-rarefied datasets
1.3.6) Beta diversity
1.3.6.1) Dissimilarity matrix using Bray-Curtis index:
The Bray-Curtis dissimilarity, constrained between 0 (minimum distance) and 1 (highest dissimilarity) allows us to quantify the differences between samples according to the composition and relative abundance of their OTUs. In our dataset, most of the samples pairs take dissimilarity values between between 2 and 4, meaning that their composition is substantially similar.
1.3.6.2) Hierarchical clustering
The stations seem to form clusters according to geographic localization, but there are no evident clusters separated from the general groups.
(To be done: assign Longhurst provinces information to each station and check if any of the central clusters is meaningful regarding to the samples’ geographical location)
1.3.6.3) Non-metric multidimensional scaling
We can identify a prominent group in the central part of the NMDS plot and a few outliers in the central-high edge of the plot. The stress parameter takes a value below 0.2, suggesting that the plot is acceptable.
##
## Call:
## monoMDS(dist = tb16_tax_occur_ss16509_no_cero.bray)
##
## Non-metric Multidimensional Scaling
##
## 91 points, dissimilarity 'bray', call 'vegdist(x = tb16_tax_occur_ss16509_no_cero, method = "bray")'
##
## Dimensions: 2
## Stress: 0.1440638
## Stress type 1, weak ties
## Scores scaled to unit root mean square, rotated to principal components
## Stopped after 69 iterations: Stress nearly unchanged (ratio > sratmax)
When implementing a most robut function for computing NMDS plots, the result is quiet the same:
## Run 0 stress 0.1181392
## Run 1 stress 0.1261599
## Run 2 stress 0.1440161
## Run 3 stress 0.1340847
## Run 4 stress 0.1606356
## Run 5 stress 0.1181443
## ... Procrustes: rmse 0.0004856157 max resid 0.003283112
## ... Similar to previous best
## Run 6 stress 0.1228213
## Run 7 stress 0.1572189
## Run 8 stress 0.1646623
## Run 9 stress 0.1527362
## Run 10 stress 0.1346715
## Run 11 stress 0.1545479
## Run 12 stress 0.1181442
## ... Procrustes: rmse 0.0004871289 max resid 0.003309532
## ... Similar to previous best
## Run 13 stress 0.146291
## Run 14 stress 0.1588475
## Run 15 stress 0.1228214
## Run 16 stress 0.145217
## Run 17 stress 0.1375357
## Run 18 stress 0.1453191
## Run 19 stress 0.127459
## Run 20 stress 0.1374571
## *** Solution reached## Warning in ordiplot(x, choices = choices, type = type, display = display, :
## Species scores not available
1.4) Geographical analysis
Communities quickly change their composition across geographical distances:
1.4.1) Mantel correlograms
Mantel statistic is -significantlly- so low, meaning that the correlation between samples dissimilarity and geographical distances is weak.
##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = geo_distances_MP_18S, ydis = tb16_tax_occur_ss16509_no_cero.bray)
##
## Mantel statistic r: 0.108
## Significance: 0.001
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.0201 0.0283 0.0361 0.0410
## Permutation: free
## Number of permutations: 999Correlograms:
MP_18s_ss16509_mantel_correl_by_1000km<-mantel.correlog(tb16_tax_occur_ss16509_no_cero.bray, D.geo=geo_distances_MP_18S, break.pts=seq(0,20000, by=1000))
plot(MP_18s_ss16509_mantel_correl_by_1000km)
MP_18s_ss16509_mantel_correl_by_100km<-mantel.correlog(tb16_tax_occur_ss16509_no_cero.bray, D.geo=geo_distances_MP_18S, break.pts=seq(0,20000, by=100))
plot(MP_18s_ss16509_mantel_correl_by_100km)
1.5) Abundance vs. occurence
OTUs distribution according to their percentage of occurence and relative abundance.
- red line: OTUs that occur in more than 80% of the samples.
- blue line: regionally abundant OTUs (> 0.1%).
- green line: regionally rare (< 0.001%).
Regionally abundant OTUs (relative abundance over 0.1%):
## otu_names mean_rabund perc_occur SILVA_consensus
## 1 OTU_1 0.272428825 100.00000 <NA>
## 19 OTU_2 0.079407902 100.00000 <NA>
## 32 OTU_3 0.039178097 100.00000 <NA>
## 60 OTU_5 0.019931186 97.80220 <NA>
## 43 OTU_3619 0.017132846 100.00000 <NA>
## 74 OTU_6 0.017088914 100.00000 <NA>
## 90 OTU_8 0.013937120 100.00000 <NA>
## 101 OTU_9 0.010510418 100.00000 <NA>
## 48 OTU_4 0.009750259 87.91209 <NA>
## 84 OTU_7 0.009306279 98.90110 <NA>
## 62 OTU_51 0.007662154 100.00000 <NA>
## 77 OTU_62 0.006754890 100.00000 <NA>
## 8 OTU_14 0.006132519 100.00000 <NA>
## 7 OTU_13 0.005666573 100.00000 <NA>
## 4 OTU_11 0.005386339 100.00000 <NA>
## 42 OTU_36 0.005374358 45.05495 <NA>
## 51 OTU_43 0.005306463 98.90110 <NA>
## 63 OTU_52 0.004996276 100.00000 <NA>
## 21 OTU_203 0.004903086 81.31868 <NA>
## 87 OTU_75 0.004507032 89.01099 <NA>
## 2 OTU_10 0.004259415 95.60440 <NA>
## 10 OTU_16 0.004256752 94.50549 <NA>
## 9 OTU_15 0.003760187 100.00000 <NA>
## 6 OTU_12 0.003436687 97.80220 <NA>
## 75 OTU_6052 0.003297569 85.71429 <NA>
## 5 OTU_112 0.003284922 84.61538 <NA>
## 28 OTU_27 0.003217026 83.51648 <NA>
## 44 OTU_38 0.003142475 100.00000 <NA>
## 40 OTU_35 0.003125168 91.20879 <NA>
## 96 OTU_8518 0.003100540 87.91209 <NA>
## 24 OTU_23 0.003065927 97.80220 <NA>
## 16 OTU_18 0.002984719 97.80220 <NA>
## 83 OTU_6983 0.002900183 84.61538 <NA>
## 33 OTU_30 0.002793015 98.90110 <NA>
## 37 OTU_33 0.002786359 87.91209 <NA>
## 13 OTU_17 0.002757071 97.80220 <NA>
## 18 OTU_19 0.002601312 94.50549 <NA>
## 25 OTU_24 0.002600646 96.70330 <NA>
## 26 OTU_26 0.002582674 97.80220 <NA>
## 39 OTU_34 0.002564702 92.30769 <NA>
## 29 OTU_2754 0.002532751 98.90110 <NA>
## 22 OTU_21 0.002474841 64.83516 <NA>
## 66 OTU_5345 0.002467519 86.81319 <NA>
## 53 OTU_45 0.002409608 91.20879 <NA>
## 72 OTU_5713 0.002185954 89.01099 <NA>
## 97 OTU_86 0.002120721 96.70330 <NA>
## 34 OTU_303 0.002087439 98.90110 <NA>
## 35 OTU_31 0.001960303 91.20879 <NA>
## 89 OTU_77 0.001953646 75.82418 <NA>
## 92 OTU_8015 0.001932346 100.00000 <NA>
## 78 OTU_6249 0.001923693 100.00000 <NA>
## 11 OTU_1666 0.001923027 90.10989 <NA>
## 52 OTU_44 0.001920364 95.60440 <NA>
## 98 OTU_8731 0.001887083 87.91209 <NA>
## 38 OTU_3305 0.001849807 82.41758 <NA>
## 47 OTU_3997 0.001823181 89.01099 <NA>
## 71 OTU_57 0.001821184 98.90110 <NA>
## 20 OTU_20 0.001819853 83.51648 <NA>
## 31 OTU_29 0.001781912 93.40659 <NA>
## 45 OTU_39 0.001772593 74.72527 <NA>
## 30 OTU_28 0.001756618 70.32967 <NA>
## 15 OTU_178 0.001737980 97.80220 <NA>
## 100 OTU_8904 0.001702701 100.00000 <NA>
## 65 OTU_53 0.001675410 69.23077 <NA>
## 55 OTU_47 0.001669419 94.50549 <NA>
## 17 OTU_182 0.001668753 95.60440 <NA>
## 41 OTU_350 0.001605518 97.80220 <NA>
## 12 OTU_167 0.001552932 79.12088 <NA>
## 50 OTU_42 0.001531632 84.61538 <NA>
## 54 OTU_46 0.001512329 84.61538 <NA>
## 56 OTU_48 0.001474387 82.41758 <NA>
## 64 OTU_5214 0.001439774 98.90110 <NA>
## 27 OTU_269 0.001436446 71.42857 <NA>
## 49 OTU_40 0.001391848 85.71429 <NA>
## 95 OTU_8415 0.001376539 98.90110 <NA>
## 102 OTU_9607 0.001349913 96.70330 <NA>
## 94 OTU_836 0.001349247 94.50549 <NA>
## 85 OTU_72 0.001341925 95.60440 <NA>
## 61 OTU_5092 0.001331941 83.51648 <NA>
## 14 OTU_170 0.001272033 85.71429 <NA>
## 3 OTU_101 0.001246739 68.13187 <NA>
## 86 OTU_73 0.001242745 20.87912 <NA>
## 70 OTU_5677 0.001230764 98.90110 <NA>
## 67 OTU_54 0.001208798 93.40659 <NA>
## 58 OTU_49 0.001182172 93.40659 <NA>
## 68 OTU_55 0.001180841 60.43956 <NA>
## 69 OTU_5631 0.001176182 18.68132 <NA>
## 23 OTU_22 0.001172853 87.91209 <NA>
## 76 OTU_61 0.001168194 97.80220 <NA>
## 82 OTU_69 0.001155547 53.84615 <NA>
## 81 OTU_65 0.001151553 73.62637 <NA>
## 93 OTU_8316 0.001150887 87.91209 <NA>
## 80 OTU_63 0.001146228 73.62637 <NA>
## 59 OTU_497 0.001140903 31.86813 <NA>
## 46 OTU_396 0.001136909 100.00000 <NA>
## 36 OTU_32 0.001125593 73.62637 <NA>
## 88 OTU_7628 0.001118937 57.14286 <NA>
## 73 OTU_59 0.001087652 90.10989 <NA>
## 57 OTU_4850 0.001078333 89.01099 <NA>
## 79 OTU_627 0.001042388 100.00000 <NA>
## 91 OTU_80 0.001039726 42.85714 <NA>
## 99 OTU_89 0.001003116 86.81319 <NA>
## SILVA_classif
## 1 KC002097.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_unidentified_marine_bacterioplankton
## 19 KM520635.1.1287_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 32 AACY020285848.922.2246_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_marine_metagenome
## 60 KC001782.1.1355_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_unidentified_marine_bacterioplankton
## 43 AACY023868415.1.1427_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_marine_metagenome
## 74 KC000519.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 90 KC002744.1.1344_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 101 KC002796.1.1323_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_2_unidentified_marine_bacterioplankton
## 48 KJ590614.1.1421_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_Sulfitobacter_uncultured_bacterium
## 84 KC001931.1.1353_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_unidentified_marine_bacterioplankton
## 62 JX945368.1.1448_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 77 KF786428.1.1342_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_SAR11_cluster_alpha_proteobacterium
## 8 DQ009267.1.1949_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_SAR116_clade_uncultured_marine_bacterium
## 7 JX945365.1.1423_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_uncultured_bacterium
## 4 DQ009111.1.2063_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_marine_bacterium
## 42 HQ233040.1.1357_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 51 KC000418.1.1315_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 63 KC002212.1.1315_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 21 KC002165.1.1315_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 87 GU061737.1.1446_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_uncultured_bacterium
## 2 KJ549180.1.1447_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Erythrobacteraceae_Erythrobacter_uncultured_bacterium
## 10 AACY020257759.244.1709_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_SAR116_clade_marine_metagenome
## 9 EU804112.1.1492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_uncultured_bacterium
## 6 JN986244.1.1428_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_uncultured_bacterium
## 75 EU802327.1.1487_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_uncultured_bacterium
## 5 EU237289.1.1306_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 28 EU802512.1.1493_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_bacterium
## 44 KC001705.1.1365_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_unidentified_marine_bacterioplankton
## 40 EU804152.1.1492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_bacterium
## 96 KC002188.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 24 AACY023498084.1.1233_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_marine_metagenome
## 16 KC001557.1.1292_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_unidentified_marine_bacterioplankton
## 83 KC001872.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_unidentified_marine_bacterioplankton
## 33 JN986006.1.1452_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_4_uncultured_bacterium
## 37 DQ009141.1.1996_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_uncultured_marine_bacterium
## 13 EU237459.1.1302_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodospirillales_Rhodospirillaceae_AEGEAN-169_marine_group_uncultured_bacterium
## 18 KJ549185.1.1449_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Erythrobacteraceae_Erythrobacter_uncultured_bacterium
## 25 EU802705.1.1252_Bacteria_Bacteroidetes_Cytophagia_Cytophagales_Flammeovirgaceae_Marinoscillum_uncultured_bacterium
## 26 KC002130.1.1290_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_unidentified_marine_bacterioplankton
## 39 FN433299.1.1479_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_Flavobacteriia_bacterium
## 29 HQ233043.1.1357_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 22 DQ009121.1.1748_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_uncultured_marine_bacterium
## 66 KC002668.1.1343_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 53 KC002674.1.1343_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 72 JX945368.1.1448_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 97 JN985994.1.1438_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 34 EU802406.1.1257_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 35 AACY020563509.792.2305_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 89 FN433412.1.1496_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_Flavobacteriia_bacterium
## 92 EU804784.1.1433_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 78 KC002895.1.1345_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 11 AY664087.1.1207_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 52 AQSI01000003.54241.55782_Bacteria_Marinimicrobia__SAR406_clade__Marinimicrobia_bacterium_SCGC_AAA298-D23
## 98 KF786624.1.1388_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_Flavobacteriales_bacterium
## 38 JN986032.1.1449_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 47 JN986032.1.1449_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 71 EU805317.1.1450_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_SAR116_clade_uncultured_bacterium
## 20 KC003455.1.1350_Bacteria_Proteobacteria_Gammaproteobacteria_Alteromonadales_Alteromonadaceae_Alteromonas_unidentified_marine_bacterioplankton
## 31 EU804109.1.1485_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_NS9_marine_group_uncultured_bacterium
## 45 KC000407.1.1363_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_Formosa_unidentified_marine_bacterioplankton
## 30 JX105591.1.1377_Bacteria_Actinobacteria_Actinobacteria_Corynebacteriales_Nocardiaceae_Rhodococcus_uncultured_bacterium
## 15 JNAU01000004.222174.223638_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_Prochlorococcus_sp._MIT_0601
## 100 EU804476.1.1440_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 65 AACY020549891.3846.5359_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 55 JN986342.1.1465_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_S25-593_uncultured_bacterium
## 17 DQ396183.1.1451_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Erythrobacteraceae_Erythrobacter_uncultured_organism
## 41 KC002791.1.1322_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 12 JN166214.1.1446_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_marine_microorganism
## 50 EU804751.1.1482_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_NS7_marine_group_uncultured_bacterium
## 54 EU803106.1.1287_Bacteria_Proteobacteria_Deltaproteobacteria_SAR324_clade_Marine_group_B__uncultured_bacterium
## 56 AACY020562322.3851.5364_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 64 EF572784.1.1439_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 27 JQ516674.1.1506_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_uncultured_actinobacterium
## 49 KF786431.1.1388_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_Flavobacteriales_bacterium
## 95 GQ346738.1.1322_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_alpha_proteobacterium
## 102 EU804974.1.1439_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 94 KM520431.1.1266_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 85 AACY020555764.489.1966_Bacteria_Proteobacteria_Alphaproteobacteria_OCS116_clade_marine_metagenome
## 61 JX945339.1.1492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_uncultured_bacterium
## 14 KC002188.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 3 JX526770.1.1401_Bacteria_Proteobacteria_Gammaproteobacteria_Thiotrichales_Thiotrichaceae_Thiothrix_uncultured_proteobacterium
## 86 AACY020462030.661.2167_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_marine_metagenome
## 70 KC294824.1.1401_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 67 AACY020481938.3418.4929_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 58 DQ009125.1.1942_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_uncultured_marine_bacterium
## 68 FQ032819.21712.23225_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_Flavobacteriia_bacterium
## 69 JN832945.1.1352_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_uncultured_bacterium
## 23 JQ032339.1.1400_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_Halomonadaceae_Halomonas_uncultured_bacterium
## 76 AACY020490277.719.2228_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_marine_metagenome
## 82 ATUR01000005.1108.2578_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Sphingomonadaceae_Sphingopyxis_Sphingopyxis_baekryungensis_DSM_16222
## 81 DQ009089.1.1878_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_NS9_marine_group_uncultured_marine_bacterium
## 93 KC002188.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 80 JQ013156.1.1423_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_Ascidiaceihabitans_uncultured_bacterium
## 59 KC811143.18830.20338_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_Candidatus_Actinomarina_minuta
## 46 KC294823.1.1400_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 36 HQ622550.1.1449_Bacteria_Proteobacteria_Alphaproteobacteria_Rhizobiales_Aurantimonadaceae_Fulvimarina_Rhizobiales_bacterium_8047
## 88 HQ233039.1.1357_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 73 EU795293.31983.33492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_uncultured_bacterium_HF0010_31F02
## 57 JX945368.1.1448_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 79 EU802825.1.1438_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 91 KC001532.1.1292_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_Ruegeria_unidentified_marine_bacterioplankton
## 99 JN018663.1.1390_Bacteria_Proteobacteria_Gammaproteobacteria_KI89A_clade_uncultured_bacteriumNumber and roportion of regionally abundant OTUs (%):
## [1] 102## [1] 1.242236
Cosmopolitan OTUs (relative abundance over 0.1% and occurence in more than 80% of samples):
## otu_names mean_rabund perc_occur SILVA_consensus PhytoREF_consensus
## 1 OTU_1 0.272428825 100.00000 <NA> <NA>
## 17 OTU_2 0.079407902 100.00000 <NA> <NA>
## 27 OTU_3 0.039178097 100.00000 <NA> <NA>
## 36 OTU_3619 0.017132846 100.00000 <NA> <NA>
## 62 OTU_6 0.017088914 100.00000 <NA> <NA>
## 72 OTU_8 0.013937120 100.00000 <NA> <NA>
## 82 OTU_9 0.010510418 100.00000 <NA> <NA>
## 53 OTU_51 0.007662154 100.00000 <NA> <NA>
## 65 OTU_62 0.006754890 100.00000 <NA> <NA>
## 7 OTU_14 0.006132519 100.00000 <NA> <NA>
## 6 OTU_13 0.005666573 100.00000 <NA> <NA>
## 3 OTU_11 0.005386339 100.00000 <NA> <NA>
## 54 OTU_52 0.004996276 100.00000 <NA> <NA>
## 8 OTU_15 0.003760187 100.00000 <NA> <NA>
## 37 OTU_38 0.003142475 100.00000 <NA> <NA>
## 73 OTU_8015 0.001932346 100.00000 <NA> <NA>
## 66 OTU_6249 0.001923693 100.00000 <NA> <NA>
## 81 OTU_8904 0.001702701 100.00000 <NA> <NA>
## 38 OTU_396 0.001136909 100.00000 <NA> <NA>
## 67 OTU_627 0.001042388 100.00000 <NA> <NA>
## 69 OTU_7 0.009306279 98.90110 <NA> <NA>
## 43 OTU_43 0.005306463 98.90110 <NA> <NA>
## 28 OTU_30 0.002793015 98.90110 <NA> <NA>
## 25 OTU_2754 0.002532751 98.90110 <NA> <NA>
## 29 OTU_303 0.002087439 98.90110 <NA> <NA>
## 59 OTU_57 0.001821184 98.90110 <NA> <NA>
## 55 OTU_5214 0.001439774 98.90110 <NA> <NA>
## 76 OTU_8415 0.001376539 98.90110 <NA> <NA>
## 58 OTU_5677 0.001230764 98.90110 <NA> <NA>
## 51 OTU_5 0.019931186 97.80220 <NA> <NA>
## 5 OTU_12 0.003436687 97.80220 <NA> <NA>
## 21 OTU_23 0.003065927 97.80220 <NA> <NA>
## 14 OTU_18 0.002984719 97.80220 <NA> <NA>
## 11 OTU_17 0.002757071 97.80220 <NA> <NA>
## 23 OTU_26 0.002582674 97.80220 <NA> <NA>
## 13 OTU_178 0.001737980 97.80220 <NA> <NA>
## 35 OTU_350 0.001605518 97.80220 <NA> <NA>
## 64 OTU_61 0.001168194 97.80220 <NA> <NA>
## 22 OTU_24 0.002600646 96.70330 <NA> <NA>
## 78 OTU_86 0.002120721 96.70330 <NA> <NA>
## 83 OTU_9607 0.001349913 96.70330 <NA> <NA>
## 2 OTU_10 0.004259415 95.60440 <NA> <NA>
## 44 OTU_44 0.001920364 95.60440 <NA> <NA>
## 15 OTU_182 0.001668753 95.60440 <NA> <NA>
## 70 OTU_72 0.001341925 95.60440 <NA> <NA>
## 9 OTU_16 0.004256752 94.50549 <NA> <NA>
## 16 OTU_19 0.002601312 94.50549 <NA> <NA>
## 47 OTU_47 0.001669419 94.50549 <NA> <NA>
## 75 OTU_836 0.001349247 94.50549 <NA> <NA>
## 26 OTU_29 0.001781912 93.40659 <NA> <NA>
## 57 OTU_54 0.001208798 93.40659 <NA> <NA>
## 50 OTU_49 0.001182172 93.40659 <NA> <NA>
## 33 OTU_34 0.002564702 92.30769 <NA> <NA>
## 34 OTU_35 0.003125168 91.20879 <NA> <NA>
## 45 OTU_45 0.002409608 91.20879 <NA> <NA>
## 30 OTU_31 0.001960303 91.20879 <NA> <NA>
## 10 OTU_1666 0.001923027 90.10989 <NA> <NA>
## 61 OTU_59 0.001087652 90.10989 <NA> <NA>
## 71 OTU_75 0.004507032 89.01099 <NA> <NA>
## 60 OTU_5713 0.002185954 89.01099 <NA> <NA>
## 39 OTU_3997 0.001823181 89.01099 <NA> <NA>
## 49 OTU_4850 0.001078333 89.01099 <NA> <NA>
## 40 OTU_4 0.009750259 87.91209 <NA> <NA>
## 77 OTU_8518 0.003100540 87.91209 <NA> <NA>
## 31 OTU_33 0.002786359 87.91209 <NA> <NA>
## 79 OTU_8731 0.001887083 87.91209 <NA> <NA>
## 20 OTU_22 0.001172853 87.91209 <NA> <NA>
## 74 OTU_8316 0.001150887 87.91209 <NA> <NA>
## 56 OTU_5345 0.002467519 86.81319 <NA> <NA>
## 80 OTU_89 0.001003116 86.81319 <NA> <NA>
## 63 OTU_6052 0.003297569 85.71429 <NA> <NA>
## 41 OTU_40 0.001391848 85.71429 <NA> <NA>
## 12 OTU_170 0.001272033 85.71429 <NA> <NA>
## 4 OTU_112 0.003284922 84.61538 <NA> <NA>
## 68 OTU_6983 0.002900183 84.61538 <NA> <NA>
## 42 OTU_42 0.001531632 84.61538 <NA> <NA>
## 46 OTU_46 0.001512329 84.61538 <NA> <NA>
## 24 OTU_27 0.003217026 83.51648 <NA> <NA>
## 18 OTU_20 0.001819853 83.51648 <NA> <NA>
## 52 OTU_5092 0.001331941 83.51648 <NA> <NA>
## 32 OTU_3305 0.001849807 82.41758 <NA> <NA>
## 48 OTU_48 0.001474387 82.41758 <NA> <NA>
## 19 OTU_203 0.004903086 81.31868 <NA> <NA>Number and proportion (%) of cosmopolitan OTUs:
## [1] 83## [1] 1.010839Number and proportion (%) of rare OTUs:
## [1] 4737## [1] 57.69092.6) Taxonomic composition analysis
2.6.1) Normalized data
No. of OTUs and reads of the rearefied dataset:
## [1] 8211## [1] 1502319No. of OTUs and reads of phototrophic groups:
## [1] 1829## [1] 770258No. of OTUs and reads of non-phototrophic groups:
## [1] 6382## [1] 732061
PHOTOTROPHS + HETEROTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 140 11 31
## Bolidophyceae 88 4 45
## Chlorarachniophyceae 10 2 7
## Chlorodendrophyceae 1 1 1
## Cryptophyceae 38 5 17
## Cyanobacteria 751849 1209 91
## Dictyochophyceae 1317 31 88
## Dinophyceae 222 15 52
## Eustigmatophyceae 116 4 37
## Mamiellophyceae 849 15 25
## Pelagophyceae 433 19 65
## Prasinophyceae_clade-IX 573 20 74
## Prasinophyceae_clade-VII 280 15 36
## Prymnesiophyceae 8924 310 91
## Pyramimonadaceae 28 3 8
## Rappemonads 39 7 25
## Trebouxiophyceae 1 1 1
## other_Prasinophyceae 11 2 10
## other_bacteria 732061 6382 91
## other_plastids 5339 155 90
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.0000 100.0000 972.5275## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 9.318926e-03 0.13396663 34.065934
## Bolidophyceae 5.857611e-03 0.04871514 49.450549
## Chlorarachniophyceae 6.656376e-04 0.02435757 7.692308
## Chlorodendrophyceae 6.656376e-05 0.01217878 1.098901
## Cryptophyceae 2.529423e-03 0.06089392 18.681319
## Cyanobacteria 5.004590e+01 14.72415053 100.000000
## Dictyochophyceae 8.766447e-02 0.37754232 96.703297
## Dinophyceae 1.477715e-02 0.18268177 57.142857
## Eustigmatophyceae 7.721396e-03 0.04871514 40.659341
## Mamiellophyceae 5.651263e-02 0.18268177 27.472527
## Pelagophyceae 2.882211e-02 0.23139691 71.428571
## Prasinophyceae_clade-IX 3.814103e-02 0.24357569 81.318681
## Prasinophyceae_clade-VII 1.863785e-02 0.18268177 39.560440
## Prymnesiophyceae 5.940150e-01 3.77542321 100.000000
## Pyramimonadaceae 1.863785e-03 0.03653635 8.791209
## Rappemonads 2.595987e-03 0.08525149 27.472527
## Trebouxiophyceae 6.656376e-05 0.01217878 1.098901
## other_Prasinophyceae 7.322014e-04 0.02435757 10.989011
## other_bacteria 4.872873e+01 77.72500304 100.000000
## other_plastids 3.553839e-01 1.88771161 98.901099
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
PHOTOTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 140 11 31
## Bolidophyceae 88 4 45
## Chlorarachniophyceae 10 2 7
## Chlorodendrophyceae 1 1 1
## Cryptophyceae 38 5 17
## Cyanobacteria 751849 1209 91
## Dictyochophyceae 1317 31 88
## Dinophyceae 222 15 52
## Eustigmatophyceae 116 4 37
## Mamiellophyceae 849 15 25
## Pelagophyceae 433 19 65
## Prasinophyceae_clade-IX 573 20 74
## Prasinophyceae_clade-VII 280 15 36
## Prymnesiophyceae 8924 310 91
## Pyramimonadaceae 28 3 8
## Rappemonads 39 7 25
## Trebouxiophyceae 1 1 1
## other_Prasinophyceae 11 2 10
## other_plastids 5339 155 90
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.0000 100.0000 872.5275## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 1.817573e-02 0.60142154 34.065934
## Bolidophyceae 1.142474e-02 0.21869874 49.450549
## Chlorarachniophyceae 1.298266e-03 0.10934937 7.692308
## Chlorodendrophyceae 1.298266e-04 0.05467469 1.098901
## Cryptophyceae 4.933412e-03 0.27337343 18.681319
## Cyanobacteria 9.761002e+01 66.10169492 100.000000
## Dictyochophyceae 1.709817e-01 1.69491525 96.703297
## Dinophyceae 2.882151e-02 0.82012028 57.142857
## Eustigmatophyceae 1.505989e-02 0.21869874 40.659341
## Mamiellophyceae 1.102228e-01 0.82012028 27.472527
## Pelagophyceae 5.621493e-02 1.03881903 71.428571
## Prasinophyceae_clade-IX 7.439066e-02 1.09349371 81.318681
## Prasinophyceae_clade-VII 3.635146e-02 0.82012028 39.560440
## Prymnesiophyceae 1.158573e+00 16.94915254 100.000000
## Pyramimonadaceae 3.635146e-03 0.16402406 8.791209
## Rappemonads 5.063239e-03 0.38272280 27.472527
## Trebouxiophyceae 1.298266e-04 0.05467469 1.098901
## other_Prasinophyceae 1.428093e-03 0.10934937 10.989011
## other_plastids 6.931444e-01 8.47457627 98.901099
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
Absolute values of cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 687619 985
## Synechococcus 61215 158
## other_cyanobacteria 3015 66Relative values of Cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 91.4570612 81.472291
## Synechococcus 8.1419274 13.068652
## Other cyanobacteria 0.4010114 5.459057
PROTISTS
## [1] 621## [1] 18419occurrence_counts_phototrophs<-data.table()
nrow(tb16_phototrophs)## [1] 621#create a table per group and count in how many samples they occur.
Dinophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Dinophyceae"),]
Dinophyceae_tb_occur <- Dinophyceae_tb[,1:91]
Dinophyceae_tb_occur_len<-length(Dinophyceae_tb_occur[,colSums(Dinophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Dinophyceae",samples_per_class=Dinophyceae_tb_occur_len))
Prasinophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "other_Prasinophyceae"),]
Prasinophyceae_tb_occur <- Prasinophyceae_tb[,1:91]
Prasinophyceae_tb_occur_len<-length(Prasinophyceae_tb_occur[,colSums(Prasinophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="other_Prasinophyceae",samples_per_class=Prasinophyceae_tb_occur_len))
Chrysophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chrysophyceae"),]
Chrysophyceae_tb_occur <- Chrysophyceae_tb[,1:91]
Chrysophyceae_tb_occur_len<-length(Chrysophyceae_tb_occur[,colSums(Chrysophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chrysophyceae",samples_per_class=Chrysophyceae_tb_occur_len))
Pelagophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pelagophyceae"),]
Pelagophyceae_tb_occur <- Pelagophyceae_tb[,1:91]
Pelagophyceae_tb_occur_len<-length(Pelagophyceae_tb_occur[,colSums(Pelagophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pelagophyceae",samples_per_class=Pelagophyceae_tb_occur_len))
Dictyochophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Dictyochophyceae"),]
Dictyochophyceae_tb_occur <- Dictyochophyceae_tb[,1:91]
Dictyochophyceae_tb_occur_len<-length(Dictyochophyceae_tb_occur[,colSums(Dictyochophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Dictyochophyceae",samples_per_class=Dictyochophyceae_tb_occur_len))
Cryptophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Cryptophyceae"),]
Cryptophyceae_tb_occur <- Cryptophyceae_tb[,1:91]
Cryptophyceae_tb_occur_len<-length(Cryptophyceae_tb_occur[,colSums(Cryptophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Cryptophyceae",samples_per_class=Cryptophyceae_tb_occur_len))
Bacillariophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Bacillariophyceae"),]
Bacillariophyceae_tb_occur <- Bacillariophyceae_tb[,1:91]
Bacillariophyceae_tb_occur_len<-length(Bacillariophyceae_tb_occur[,colSums(Bacillariophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Bacillariophyceae",samples_per_class=Bacillariophyceae_tb_occur_len))
Chlorarachniophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chlorarachniophyceae"),]
Chlorarachniophyceae_tb_occur <- Chlorarachniophyceae_tb[,1:91]
Chlorarachniophyceae_tb_occur_len<-length(Chlorarachniophyceae_tb_occur[,colSums(Chlorarachniophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chlorarachniophyceae",samples_per_class=Chlorarachniophyceae_tb_occur_len))
Bolidophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Bolidophyceae"),]
Bolidophyceae_tb_occur <- Bolidophyceae_tb[,1:91]
Bolidophyceae_tb_occur_len<-length(Bolidophyceae_tb_occur[,colSums(Bolidophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Bolidophyceae",samples_per_class=Bolidophyceae_tb_occur_len))
Pinguiophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pinguiophyceae"),]
Pinguiophyceae_tb_occur <- Pinguiophyceae_tb[,1:91]
Pinguiophyceae_tb_occur_len<-length(Pinguiophyceae_tb_occur[,colSums(Pinguiophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pinguiophyceae",samples_per_class=Pinguiophyceae_tb_occur_len))
Prymnesiophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Prymnesiophyceae"),]
Prymnesiophyceae_tb_occur <- Prymnesiophyceae_tb[,1:91]
Prymnesiophyceae_tb_occur_len<-length(Prymnesiophyceae_tb_occur[,colSums(Prymnesiophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Prymnesiophyceae",samples_per_class=Prymnesiophyceae_tb_occur_len))
Mamiellophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Mamiellophyceae"),]
Mamiellophyceae_tb_occur <- Mamiellophyceae_tb[,1:91]
Mamiellophyceae_tb_occur_len<-length(Mamiellophyceae_tb_occur[,colSums(Mamiellophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Mamiellophyceae",samples_per_class=Mamiellophyceae_tb_occur_len))
Eustigmatophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Eustigmatophyceae"),]
Eustigmatophyceae_tb_occur <- Eustigmatophyceae_tb[,1:91]
Eustigmatophyceae_tb_occur_len<-length(Eustigmatophyceae_tb_occur[,colSums(Eustigmatophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Eustigmatophyceae",samples_per_class=Eustigmatophyceae_tb_occur_len))
Chlorophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chlorophyceae"),]
Chlorophyceae_tb_occur <- Chlorophyceae_tb[,1:91]
Chlorophyceae_tb_occur_len<-length(Chlorophyceae_tb_occur[,colSums(Chlorophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chlorophyceae",samples_per_class=Chlorophyceae_tb_occur_len))
Ulvophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Ulvophyceae"),]
Ulvophyceae_tb_occur <- Ulvophyceae_tb[,1:91]
Ulvophyceae_tb_occur_len<-length(Ulvophyceae_tb_occur[,colSums(Ulvophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Ulvophyceae",samples_per_class=Ulvophyceae_tb_occur_len))
Raphydophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Raphydophyceae"),]
Raphydophyceae_tb_occur <- Raphydophyceae_tb[,1:91]
Raphydophyceae_tb_occur_len<-length(Raphydophyceae_tb_occur[,colSums(Raphydophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Raphydophyceae",samples_per_class=Raphydophyceae_tb_occur_len))
Trebouxiophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Trebouxiophyceae"),]
Trebouxiophyceae_tb_occur <- Trebouxiophyceae_tb[,1:91]
Trebouxiophyceae_tb_occur_len<-length(Trebouxiophyceae_tb_occur[,colSums(Trebouxiophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Trebouxiophyceae",samples_per_class=Trebouxiophyceae_tb_occur_len))
Phaeophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Phaeophyceae"),]
Phaeophyceae_tb_occur <- Phaeophyceae_tb[,1:91]
Phaeophyceae_tb_occur_len<-length(Phaeophyceae_tb_occur[,colSums(Phaeophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Phaeophyceae",samples_per_class=Phaeophyceae_tb_occur_len))
Phaeothamniophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Phaeothamniophyceae"),]
Phaeothamniophyceae_tb_occur <- Phaeothamniophyceae_tb[,1:91]
Phaeothamniophyceae_tb_occur_len<-length(Phaeothamniophyceae_tb_occur[,colSums(Phaeothamniophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Phaeothamniophyceae",samples_per_class=Phaeothamniophyceae_tb_occur_len))
Xanthophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Xanthophyceae"),]
Xanthophyceae_tb_occur <- Xanthophyceae_tb[,1:91]
Xanthophyceae_tb_occur_len<-length(Xanthophyceae_tb_occur[,colSums(Xanthophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Xanthophyceae",samples_per_class=Xanthophyceae_tb_occur_len))
Chlorodendrophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chlorodendrophyceae"),]
Chlorodendrophyceae_tb_occur <- Chlorodendrophyceae_tb[,1:91]
Chlorodendrophyceae_tb_occur_len<-length(Chlorodendrophyceae_tb_occur[,colSums(Chlorodendrophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chlorodendrophyceae",samples_per_class=Chlorodendrophyceae_tb_occur_len))
IncertaeSedis_Archaeplastida_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "IncertaeSedis_Archaeplastida"),]
IncertaeSedis_Archaeplastida_tb_occur <- IncertaeSedis_Archaeplastida_tb[,1:91]
IncertaeSedis_Archaeplastida_tb_occur_len<-length(IncertaeSedis_Archaeplastida_tb_occur[,colSums(IncertaeSedis_Archaeplastida_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="IncertaeSedis_Archaeplastida",samples_per_class=IncertaeSedis_Archaeplastida_tb_occur_len))
Nephroselmidophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Nephroselmidophyceae"),]
Nephroselmidophyceae_tb_occur <- Nephroselmidophyceae_tb[,1:91]
Nephroselmidophyceae_tb_occur_len<-length(Nephroselmidophyceae_tb_occur[,colSums(Nephroselmidophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Nephroselmidophyceae",samples_per_class=Nephroselmidophyceae_tb_occur_len))
Pavlovophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pavlovophyceae"),]
Pavlovophyceae_tb_occur <- Pavlovophyceae_tb[,1:91]
Pavlovophyceae_tb_occur_len<-length(Pavlovophyceae_tb_occur[,colSums(Pavlovophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pavlovophyceae",samples_per_class=Pavlovophyceae_tb_occur_len))
Rhodophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Rhodophyceae"),]
Rhodophyceae_tb_occur <- Rhodophyceae_tb[,1:91]
Rhodophyceae_tb_occur_len<-length(Rhodophyceae_tb_occur[,colSums(Rhodophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Rhodophyceae",samples_per_class=Rhodophyceae_tb_occur_len))
Rappemonads_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Rappemonads"),]
Rappemonads_tb_occur <- Rappemonads_tb[,1:91]
Rappemonads_tb_occur_len<-length(Rappemonads_tb_occur[,colSums(Rappemonads_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Rappemonads",samples_per_class=Rappemonads_tb_occur_len))
MOCH_1_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "MOCH_1"),]
MOCH_1_tb_occur <- MOCH_1_tb[,1:91]
MOCH_1_tb_occur_len<-length(MOCH_1_tb_occur[,colSums(MOCH_1_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="MOCH_1",samples_per_class=MOCH_1_tb_occur_len))
MOCH_2_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "MOCH_2"),]
MOCH_2_tb_occur <- MOCH_2_tb[,1:91]
MOCH_2_tb_occur_len<-length(MOCH_2_tb_occur[,colSums(MOCH_2_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="MOCH_2",samples_per_class=MOCH_2_tb_occur_len))
MOCH_5_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "MOCH_5"),]
MOCH_5_tb_occur <- MOCH_5_tb[,1:91]
MOCH_5_tb_occur_len<-length(MOCH_5_tb_occur[,colSums(MOCH_5_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="MOCH_5",samples_per_class=MOCH_5_tb_occur_len))
Prasinophyceae_clade_VII_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Prasinophyceae_clade-VII"),]
Prasinophyceae_clade_VII_tb_occur <- Prasinophyceae_clade_VII_tb[,1:91]
Prasinophyceae_clade_VII_tb_occur_len<-length(Prasinophyceae_clade_VII_tb_occur[,colSums(Prasinophyceae_clade_VII_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Prasinophyceae_clade-VII",samples_per_class=Prasinophyceae_clade_VII_tb_occur_len))
Prasinophyceae_clade_IX_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Prasinophyceae_clade-IX"),]
Prasinophyceae_clade_IX_tb_occur <- Prasinophyceae_clade_IX_tb[,1:91]
Prasinophyceae_clade_IX_tb_occur_len<-length(Prasinophyceae_clade_IX_tb_occur[,colSums(Prasinophyceae_clade_IX_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Prasinophyceae_clade-IX",samples_per_class=Prasinophyceae_clade_IX_tb_occur_len))
Pyramimonadaceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pyramimonadaceae"),]
Pyramimonadaceae_tb_occur <- Pyramimonadaceae_tb[,1:91]
Pyramimonadaceae_tb_occur_len<-length(Pyramimonadaceae_tb_occur[,colSums(Pyramimonadaceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pyramimonadaceae",samples_per_class=Pyramimonadaceae_tb_occur_len))
other_plastids_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "other_plastids"),]
other_plastids_tb_occur <- other_plastids_tb[,1:91]
other_plastids_tb_occur_len<-length(other_plastids_tb_occur[,colSums(other_plastids_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="other_plastids",samples_per_class=other_plastids_tb_occur_len))
occurrence_counts_phototrophs## group samples_per_class
## 1: Dinophyceae 0
## 2: other_Prasinophyceae 0
## 3: Chrysophyceae 0
## 4: Pelagophyceae 0
## 5: Dictyochophyceae 0
## 6: Cryptophyceae 0
## 7: Bacillariophyceae 0
## 8: Chlorarachniophyceae 0
## 9: Bolidophyceae 0
## 10: Pinguiophyceae 0
## 11: Prymnesiophyceae 0
## 12: Mamiellophyceae 0
## 13: Eustigmatophyceae 0
## 14: Chlorophyceae 0
## 15: Ulvophyceae 0
## 16: Raphydophyceae 0
## 17: Trebouxiophyceae 0
## 18: Phaeophyceae 0
## 19: Phaeothamniophyceae 0
## 20: Xanthophyceae 0
## 21: Chlorodendrophyceae 0
## 22: IncertaeSedis_Archaeplastida 0
## 23: Nephroselmidophyceae 0
## 24: Pavlovophyceae 0
## 25: Rhodophyceae 0
## 26: Rappemonads 0
## 27: MOCH_1 0
## 28: MOCH_2 0
## 29: MOCH_5 0
## 30: Prasinophyceae_clade-VII 0
## 31: Prasinophyceae_clade-IX 0
## 32: Pyramimonadaceae 0
## 33: other_plastids 90
## group samples_per_class#row.names(occurrence_counts_phototrophs)<-occurrence_counts_phototrophs$group
occurrence_counts_phototrophs<-as.data.frame(occurrence_counts_phototrophs)Absolute values
## reads_per_class OTUs_per_class samples_per_class
## 13113 470 NA
## other_plastids 5306 151 90
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100 100 NA## reads_per_class OTUs_per_class samples_per_class
## 71.19279 75.68438 NA
## other_plastids 28.80721 24.31562 98.9011
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
2.6.2) Non-rarefied data
No. of OTUs and reads of the rearefied dataset:
## [1] 8881## [1] 4745946No. of OTUs and reads of phototrophic groups:
## [1] 1952## [1] 2504586No. of OTUs and reads of non-phototrophic groups:
## [1] 6929## [1] 2241360
PHOTOTROPHS + HETEROTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 450 14 42
## Bolidophyceae 271 4 61
## Chlorarachniophyceae 25 2 16
## Chlorodendrophyceae 8 1 5
## Cryptophyceae 103 6 24
## Cyanobacteria 2449889 1278 91
## Dictyochophyceae 4003 31 89
## Dinophyceae 819 17 73
## Eustigmatophyceae 369 6 56
## Mamiellophyceae 1248 16 34
## Pelagophyceae 1076 19 75
## Prasinophyceae_clade-IX 1785 23 80
## Prasinophyceae_clade-VII 923 16 49
## Prymnesiophyceae 26832 336 91
## Pyramimonadaceae 51 3 17
## Rappemonads 136 7 41
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 27 2 16
## other_bacteria 2241360 6929 91
## other_plastids 16569 170 90
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.000 100.000 1145.055## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 9.481777e-03 0.15763991 46.153846
## Bolidophyceae 5.710137e-03 0.04503997 67.032967
## Chlorarachniophyceae 5.267654e-04 0.02251999 17.582418
## Chlorodendrophyceae 1.685649e-04 0.01125999 5.494505
## Cryptophyceae 2.170273e-03 0.06755996 26.373626
## Cyanobacteria 5.162067e+01 14.39027137 100.000000
## Dictyochophyceae 8.434567e-02 0.34905979 97.802198
## Dinophyceae 1.725683e-02 0.19141989 80.219780
## Eustigmatophyceae 7.775057e-03 0.06755996 61.538462
## Mamiellophyceae 2.629613e-02 0.18015989 37.362637
## Pelagophyceae 2.267198e-02 0.21393987 82.417582
## Prasinophyceae_clade-IX 3.761105e-02 0.25897984 87.912088
## Prasinophyceae_clade-VII 1.944818e-02 0.18015989 53.846154
## Prymnesiophyceae 5.653667e-01 3.78335773 100.000000
## Pyramimonadaceae 1.074601e-03 0.03377998 18.681319
## Rappemonads 2.865604e-03 0.07881995 45.054945
## Trebouxiophyceae 4.214123e-05 0.01125999 1.098901
## other_Prasinophyceae 5.689066e-04 0.02251999 17.582418
## other_bacteria 4.722683e+01 78.02049319 100.000000
## other_plastids 3.491190e-01 1.91419885 98.901099
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
PHOTOTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 450 14 42
## Bolidophyceae 271 4 61
## Chlorarachniophyceae 25 2 16
## Chlorodendrophyceae 8 1 5
## Cryptophyceae 103 6 24
## Cyanobacteria 2449889 1278 91
## Dictyochophyceae 4003 31 89
## Dinophyceae 819 17 73
## Eustigmatophyceae 369 6 56
## Mamiellophyceae 1248 16 34
## Pelagophyceae 1076 19 75
## Prasinophyceae_clade-IX 1785 23 80
## Prasinophyceae_clade-VII 923 16 49
## Prymnesiophyceae 26832 336 91
## Pyramimonadaceae 51 3 17
## Rappemonads 136 7 41
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 27 2 16
## other_plastids 16569 170 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.000 100.000 1046.154## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 1.796704e-02 0.71721311 46.153846
## Bolidophyceae 1.082015e-02 0.20491803 67.032967
## Chlorarachniophyceae 9.981690e-04 0.10245902 17.582418
## Chlorodendrophyceae 3.194141e-04 0.05122951 5.494505
## Cryptophyceae 4.112456e-03 0.30737705 26.373626
## Cyanobacteria 9.781613e+01 65.47131148 100.000000
## Dictyochophyceae 1.598268e-01 1.58811475 97.802198
## Dinophyceae 3.270002e-02 0.87090164 80.219780
## Eustigmatophyceae 1.473297e-02 0.30737705 61.538462
## Mamiellophyceae 4.982859e-02 0.81967213 37.362637
## Pelagophyceae 4.296119e-02 0.97336066 82.417582
## Prasinophyceae_clade-IX 7.126926e-02 1.17827869 87.912088
## Prasinophyceae_clade-VII 3.685240e-02 0.81967213 53.846154
## Prymnesiophyceae 1.071315e+00 17.21311475 100.000000
## Pyramimonadaceae 2.036265e-03 0.15368852 18.681319
## Rappemonads 5.430039e-03 0.35860656 45.054945
## Trebouxiophyceae 7.985352e-05 0.05122951 1.098901
## other_Prasinophyceae 1.078022e-03 0.10245902 17.582418
## other_plastids 6.615465e-01 8.70901639 100.000000
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
Absolute values of cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 2287847 1036
## Synechococcus 152047 167
## other_cyanobacteria 9995 75Relative values of Cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 93.3857411 81.064163
## Synechococcus 6.2062812 13.067293
## Other cyanobacteria 0.4079777 5.868545
PROTISTS
## [1] 675## [1] 54727Absolute values
## reads_per_class OTUs_per_class samples_per_class
## 38247 509 NA
## other_plastids 16480 166 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100 100 NA## reads_per_class OTUs_per_class samples_per_class
## 69.88689 75.40741 NA
## other_plastids 30.11311 24.59259 100
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
